A Fuzzy Co-Clustering approach for Clickstream Data Pattern

TL;DR

This paper introduces a fuzzy co-clustering algorithm for clickstream data to identify user groups with similar web navigation patterns, aiding targeted marketing and improving business insights.

Contribution

It proposes a novel fuzzy co-clustering method specifically designed for clickstream data to uncover user-web page interest associations.

Findings

Algorithm effectively identifies user interest groups.

Experimental results demonstrate improved clustering accuracy.

Potential applications in targeted marketing strategies.

Abstract

Web Usage mining is a very important tool to extract the hidden business intelligence data from large databases. The extracted information provides the organizations with the ability to produce results more effectively to improve their businesses and increasing of sales. Co-clustering is a powerful bipartition technique which identifies group of users associated to group of web pages. These associations are quantified to reveal the users' interest in the different web pages' clusters. In this paper, Fuzzy Co-Clustering algorithm is proposed for clickstream data to identify the subset of users of similar navigational behavior /interest over a subset of web pages of a website. Targeting the users group for various promotional activities is an important aspect of marketing practices. Experiments are conducted on real dataset to prove the efficiency of proposed algorithm. The results and findings of this algorithm could be used to enhance the marketing strategy for directing marketing, advertisements for web based businesses and so on.